{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.simplefilter(action = \"ignore\", category = FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetch the data and load it in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from urllib.request import urlretrieve\n",
    "\n",
    "url = (\"https://archive.ics.uci.edu/ml/machine-learning-databases\"\n",
    "       \"/adult/adult.data\")\n",
    "local_filename = os.path.basename(url)\n",
    "if not os.path.exists(local_filename):\n",
    "    print(\"Downloading Adult Census datasets from UCI\")\n",
    "    urlretrieve(url, local_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names = (\"age, workclass, fnlwgt, education, education-num, \"\n",
    "         \"marital-status, occupation, relationship, race, sex, \"\n",
    "         \"capital-gain, capital-loss, hours-per-week, \"\n",
    "         \"native-country, income\").split(', ')    \n",
    "data = pd.read_csv(local_filename, names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  income  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  \n",
       "2             0             0              40   United-States   <=50K  \n",
       "3             0             0              40   United-States   <=50K  \n",
       "4             0             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               32561\n",
       "workclass         32561\n",
       "fnlwgt            32561\n",
       "education         32561\n",
       "education-num     32561\n",
       "marital-status    32561\n",
       "occupation        32561\n",
       "relationship      32561\n",
       "race              32561\n",
       "sex               32561\n",
       "capital-gain      32561\n",
       "capital-loss      32561\n",
       "hours-per-week    32561\n",
       "native-country    32561\n",
       "income            32561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32561.000000</td>\n",
       "      <td>3.256100e+04</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.581647</td>\n",
       "      <td>1.897784e+05</td>\n",
       "      <td>10.080679</td>\n",
       "      <td>1077.648844</td>\n",
       "      <td>87.303830</td>\n",
       "      <td>40.437456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.640433</td>\n",
       "      <td>1.055500e+05</td>\n",
       "      <td>2.572720</td>\n",
       "      <td>7385.292085</td>\n",
       "      <td>402.960219</td>\n",
       "      <td>12.347429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.178270e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.783560e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.370510e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.484705e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        fnlwgt  education-num  capital-gain  capital-loss  \\\n",
       "count  32561.000000  3.256100e+04   32561.000000  32561.000000  32561.000000   \n",
       "mean      38.581647  1.897784e+05      10.080679   1077.648844     87.303830   \n",
       "std       13.640433  1.055500e+05       2.572720   7385.292085    402.960219   \n",
       "min       17.000000  1.228500e+04       1.000000      0.000000      0.000000   \n",
       "25%       28.000000  1.178270e+05       9.000000      0.000000      0.000000   \n",
       "50%       37.000000  1.783560e+05      10.000000      0.000000      0.000000   \n",
       "75%       48.000000  2.370510e+05      12.000000      0.000000      0.000000   \n",
       "max       90.000000  1.484705e+06      16.000000  99999.000000   4356.000000   \n",
       "\n",
       "       hours-per-week  \n",
       "count    32561.000000  \n",
       "mean        40.437456  \n",
       "std         12.347429  \n",
       "min          1.000000  \n",
       "25%         40.000000  \n",
       "50%         40.000000  \n",
       "75%         45.000000  \n",
       "max         99.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation\n",
       " Prof-specialty       4140\n",
       " Craft-repair         4099\n",
       " Exec-managerial      4066\n",
       " Adm-clerical         3770\n",
       " Sales                3650\n",
       " Other-service        3295\n",
       " Machine-op-inspct    2002\n",
       " ?                    1843\n",
       " Transport-moving     1597\n",
       " Handlers-cleaners    1370\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('occupation').size().nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "native-country\n",
       " United-States    29170\n",
       " Mexico             643\n",
       " ?                  583\n",
       " Philippines        198\n",
       " Germany            137\n",
       " Canada             121\n",
       " Puerto-Rico        114\n",
       " El-Salvador        106\n",
       " India              100\n",
       " Cuba                95\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('native-country').size().nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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COTM6/X0Igz2Oy5Ytu6Oqluxvu5lTmh1Icg7wSFXdkWTpRNtUVSWZevu051sHrANYsmRJ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\nvV1yxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd430668400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(column='education-num', bins=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3DEtSwywBSWqYJSBJDbMEJKlhloAkNcwSkKSGWQKS1DBLQJIaZglIUsMsAUlqmCUgSQ2z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\nwywBSWqYJSBJDbMEJKlhloAkNez/Ac8ggNJsg/UaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4089f6eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(column='age', bins=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd40889bf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist('hours-per-week', bins=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nmAn45UVFlmryVPPjVxuuqpbTQrMsMhIFuYAfv1ijtWTdWLSE1scV6mXVkkpo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4087a28d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='age', y='hours-per-week', kind='scatter',\n",
    "          alpha=0.02, s=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income\n",
       " <=50K    24720\n",
       " >50K      7841\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('income')['income'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24080955744602439"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(data['income'] == ' >50K')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' >50K'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['income'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' >50K'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['income'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' >50K'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_names = data['income'].unique()\n",
    "target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd408885550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "low_income = data[data['income'] == ' <=50K']\n",
    "high_income = data[data['income'] == ' >50K']\n",
    "\n",
    "bins = np.linspace(10, 90, 20)\n",
    "plt.hist(low_income['age'].values, bins=bins, alpha=0.5, label='<=50K')\n",
    "plt.hist(high_income['age'].values, bins=bins, alpha=0.5, label='>50K')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ztH633x/7SSqfAePZ+mimLPz1wcD/X+vxbN5a6PYMtbpfiksZeDXRoI92hlwP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/9dOX79D7o0d2PA4N0/WOQ3/99N4qhDNqrzE//9Av/+jz5EWGiSlxl++Se/hf/pl9/PKNlBz8C/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\n/wdxhQYB+kB1dAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4084089b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(low_income['age'], low_income['hours-per-week'],\n",
    "            alpha=0.03, s=50, c='b', label='<=50K');\n",
    "plt.scatter(high_income['age'], high_income['hours-per-week'],\n",
    "            alpha=0.03, s=50, c='g', label='>50K');\n",
    "plt.legend()\n",
    "plt.xlabel('age'); plt.ylabel('hours-per-week');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building predictive models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target = data['income']\n",
    "features_data = data.drop('income', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'fnlwgt',\n",
       " 'education-num',\n",
       " 'capital-gain',\n",
       " 'capital-loss',\n",
       " 'hours-per-week']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_features = [c for c in features_data if features_data[c].dtype.kind in ('i', 'f')]\n",
    "numeric_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>77516</td>\n",
       "      <td>13</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>83311</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>234721</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  fnlwgt  education-num  capital-gain  capital-loss  hours-per-week\n",
       "0   39   77516             13          2174             0              40\n",
       "1   50   83311             13             0             0              13\n",
       "2   38  215646              9             0             0              40\n",
       "3   53  234721              7             0             0              40\n",
       "4   28  338409             13             0             0              40"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_data = features_data[numeric_features]\n",
    "numeric_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           workclass   education       marital-status          occupation  \\\n",
       "0          State-gov   Bachelors        Never-married        Adm-clerical   \n",
       "1   Self-emp-not-inc   Bachelors   Married-civ-spouse     Exec-managerial   \n",
       "2            Private     HS-grad             Divorced   Handlers-cleaners   \n",
       "3            Private        11th   Married-civ-spouse   Handlers-cleaners   \n",
       "4            Private   Bachelors   Married-civ-spouse      Prof-specialty   \n",
       "\n",
       "     relationship    race      sex  native-country  \n",
       "0   Not-in-family   White     Male   United-States  \n",
       "1         Husband   White     Male   United-States  \n",
       "2   Not-in-family   White     Male   United-States  \n",
       "3         Husband   Black     Male   United-States  \n",
       "4            Wife   Black   Female            Cuba  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data = features_data.drop(numeric_features, 1)\n",
    "categorical_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   workclass  education  marital-status  occupation  relationship  race  sex  \\\n",
       "0          0          0               0           0             0     0    0   \n",
       "1          1          0               1           1             1     0    0   \n",
       "2          2          1               2           2             0     0    0   \n",
       "3          2          2               1           2             1     1    0   \n",
       "4          2          0               1           3             2     1    1   \n",
       "\n",
       "   native-country  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data_encoded = categorical_data.apply(lambda x: pd.factorize(x)[0])\n",
    "categorical_data_encoded.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>77516</td>\n",
       "      <td>13</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>83311</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>234721</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  fnlwgt  education-num  capital-gain  capital-loss  hours-per-week  \\\n",
       "0   39   77516             13          2174             0              40   \n",
       "1   50   83311             13             0             0              13   \n",
       "2   38  215646              9             0             0              40   \n",
       "3   53  234721              7             0             0              40   \n",
       "4   28  338409             13             0             0              40   \n",
       "\n",
       "   workclass  education  marital-status  occupation  relationship  race  sex  \\\n",
       "0          0          0               0           0             0     0    0   \n",
       "1          1          0               1           1             1     0    0   \n",
       "2          2          1               2           2             0     0    0   \n",
       "3          2          2               1           2             1     1    0   \n",
       "4          2          0               1           3             2     1    1   \n",
       "\n",
       "   native-country  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = pd.concat([numeric_data, categorical_data_encoded], axis=1)\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Alternatively: use one-hot encoding for categorical features\n",
    "# features = pd.get_dummies(features_data)\n",
    "# features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = features.values.astype(np.float32)\n",
    "y = (target.values == ' >50K').astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 14)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 1], dtype=int32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Decision Tree: 0.8967 +/-0.0036\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "clf = DecisionTreeClassifier(max_depth=8)\n",
    "\n",
    "scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='roc_auc')\n",
    "print(\"ROC AUC Decision Tree: {:.4f} +/-{:.4f}\".format(\n",
    "    np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model error analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "\n",
    "def plot_learning_curve(estimator, X, y, ylim=(0, 1.1), cv=5,\n",
    "                        n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5),\n",
    "                        scoring=None):\n",
    "    plt.title(\"Learning curves for %s\" % type(estimator).__name__)\n",
    "    plt.ylim(*ylim); plt.grid()\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, validation_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes,\n",
    "        scoring=scoring)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    validation_scores_mean = np.mean(validation_scores, axis=1)\n",
    "\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, validation_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "    plt.legend(loc=\"best\")\n",
    "    print(\"Best validation score: {:.4f}\".format(validation_scores_mean[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best validation score: 0.7483\n"
     ]
    },
    {
     "data": {
      "image/png": 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S+NL2vmawDafGxPeBmZVlsQ2T9TE5tS/Wmbs3ygdwMHBC+LwV8C7B\ncBs3AT9OUL4T8DawF9AReA/ID5fNBHoDBvwDOCuNcX4AHBA371fAmPD5GOD2bMYY1p8PfEzwBZis\ntiFwKnAC8E4m2gz4L2BC+Hw4MDVNMZ4JNAmf3x4TY2Fsubh6MhJjkvjS9r5mqg3jlv8auCGLbZis\nj8mpfbGuj0Z7puDuq9x9bvh8PbCQ4NvSyQwGprj7Fnf/D8EnonpZMOzGPu4+w4N35o/AeRkOfzDw\nh/D5H2LWl80YTwPec/dltcSd8fjc/RXg8wTrTlebxdb1GHBaXc9sEsXo7s+7+/ZwcgbB93KSymSM\nSdowmZxpwyphXd8BJtdUR4bbMFkfk1P7Yl012qQQKzzl6g68Fc66OjyFfyjm1C7ZkBuHhs/j56eL\nAy+a2RwLhvMAaOtffV/jY6BtlmOE4Cgl9h8wl9oQ0ttm0WvCTnwt0CbN8X6X4IiwSsfwssfLZtY3\nJo6GjjFd72um27AvsNrdl8TMy1obxvUxu9u+WE2jTwpm1hJ4HLjG3dcRjMR6OHA8wRhLv85ieACn\nuPvxBCPGXmVmp8YuDI8csvq5YQu+fPgt4K/hrFxrw2pyoc1qYmbXA9uBSeGsVcBh4X7wQ+AvZrZP\nFkLL6fc1zoVUP0jJWhsm6GMiub4vJtKok4KZFRC8WZPc/QkAd1/t7jvcvRL4HcForpB8yI0VVD/N\nT+tQHO6+Ivz7CTAtjGd1eEpZdfr7STZjJEhYc919dRhrTrVhKJ1tFr3GzJoArYE16QjSzC4HzgGK\nww6D8HLCmvD5HIJrzUc1dIxpfl8z2YZNgPOBqTGxZ6UNE/Ux7Cb7YjKNNimE191+Dyx097ti5scO\nzT0EqPpkw9PA8PBuf0eC33iYGZ4GrjOz3mGdlwJPpSnGvc2sVdVzghuR74SxXBYWuyxmfQ0eY6ja\nUVkutWGMdLZZbF3DgH9VdeC7wswGAj8BvuXuG2PmH2jB749gZoeHMb7f0DGm+X3NSBuGTgcWuXt0\nySUbbZisj2E32BdrtCt3qXP5AZxCcNo2DygLH4OAPwHzw/lPAwfHvOZ6giOMxcR8OgYoIvgHeQ8Y\nT/hN8DTEeDjBpxHeBhYA14fz2wAvAUuAF4H9sxjj3gRHJq1j5mW1DQkS1CpgG8H115HpbDOgGcGl\nsqUEnwo5PE0xLiW4Ply1P1Z9qmRo+P6XAXOBczMdY5L40va+ZqoNw/mPAKPiymajDZP1MTm1L9b1\noWEuREQk0mgvH4mISN0pKYiISERJQUREIkoKIiISUVIQEZGIkoLkHDNrY1+NdvmxVR+5s2mKdTxs\nZkfXUuYqMytOT9S5wcxeM7Pjsx2H7L70kVTJaWZ2E1Dh7nfGzTeC/bcyK4HlKDN7DRjt7mXZjkV2\nTzpTkN2GmX3dgrHrJxF8UelgM5toZrMtGM/+hpiyr5nZ8WbWxMy+NLPbzOxtM3vTzL4WlvmFmV0T\nU/42M5tpwVj3J4Xz9zazx8P1Phaua6cjcTPrGQ7ENsfM/mFmbc2sIJw+JSxzh5ndHD6/2cxmmdk7\nZjYhTHJVcdwVrqfczIos+O2FJWGCrGqHBWY2xcwWmtmjZtY8QUxnhds714Ix+feOiaPcgoHvbk/r\nmyS7PSUF2d0cA9zt7p08GDdqjLsXAd2AM8ysU4LXtAZedvduwJsEI5QmYu7eC7gWqEowVwMfu3sn\n4OcEI2FWf5HZXsA9wFB37wH8Gfi5u28DRgATzexMYADwi/Bl97h7T6BrGN/AmCo3hdv0e+BJYFRY\nrsTCH+YhGJt/nLsfC2wGvhcX09cIxvI/zd1PIPjW7f+YWVuCb912dvfjgF8maQvZQykpyO7mPXef\nHTN9oZnNJRja4FiCzjLeJnevGqZ6DsEPsiTyRIIypwBTANy9ajiSeMcCnQmGQC8j6Izbh6+ZF77+\nKeC7YaKAYFz8mQRDnPQLX1/l6fDvfGC+BwPVbSb4QaaqgdP+4+4zwud/DuOMdRJBW7wRxlQcbtPn\nQCXwOzMbAmxI0hayh2qS7QBE6ijqxMzsSOB/gF7u/qWZ/ZlgrJh4W2Oe7yD5fr8lhTKJGDDP3fsm\nWd6FYBz8qstWLQjGtznB3VeY2S/i4q6KozLmedV0VVzxNwPjpw34p7tfslOwZkXAGcC3ge8TDMQo\nAuhMQXZv+wDrCUaYPBj4Zi3l6+N1gl/4wsy6kvhMpBw41Mx6heWamlnn8PkFQEugP3C/BWP8Nyfo\n4D+zYJTcofWIq6OZ9QyfXwS8Frf8DaBfOGJo1b2RI8P17ePuzwA/IMHlMNmz6UxBdmdzCTrkRcAy\ngg483e4D/mhm5eG6ygmO+iPuvsXMhgH3hp1+PvBrM/uU4D5Ef3dfaWYPENwPGWlmfwjrWsVXvwhY\nFwuBH4Y3vecDE+NiWm1mI4GpMR/j/SmwCXgivA+SR/CDNCIRfSRVpAYW/LBJE3ffHF6ueh440r/6\nreVsxPR14DEPfmVMJK10piBSs5bAS2FyMOB72UwIIpmmMwUREYnoRrOIiESUFEREJKKkICIiESUF\nERGJKCmIiEjk/wHHNeF6Yl9pXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3f560db00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=None)\n",
    "plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best validation score: 0.8594\n"
     ]
    },
    {
     "data": {
      "image/png": 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5j3/8Y8LtnHrqqdFPiQJxf6JURFpg81EySvuVUtqvbtfZ8Sq3ZJkZ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\njgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd40870b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=15)\n",
    "plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best validation score: 0.8966\n"
     ]
    },
    {
     "data": {
      "image/png": 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oUuy/wCB37wH8EpiSrnhERKRm6TxT6AcsdvdP3H0LMB0YHlvA3d9w96/DxdnA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3f4186b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=8)\n",
    "plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best validation score: 0.8636\n"
     ]
    },
    {
     "data": {
      "image/png": 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LS94W6LomGDbAPPhLA8Pm6aubOD2tdQ0M2zYNA4dWFo3FTbcgn7hF8/2tyyY2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\nJPL/AX8LQzRqmTvtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4083f3518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=4)\n",
    "plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import validation_curve\n",
    "\n",
    "\n",
    "def plot_validation_curve(estimator, X, y, param_name, param_range,\n",
    "                          ylim=(0, 1.1), cv=5, n_jobs=-1, scoring=None):\n",
    "    estimator_name = type(estimator).__name__\n",
    "    plt.title(\"Validation curves for %s on %s\"\n",
    "              % (param_name, estimator_name))\n",
    "    plt.ylim(*ylim); plt.grid()\n",
    "    plt.xlim(min(param_range), max(param_range))\n",
    "    plt.xlabel(param_name)\n",
    "    plt.ylabel(\"Score\")\n",
    "\n",
    "    train_scores, test_scores = validation_curve(\n",
    "        estimator, X, y, param_name, param_range,\n",
    "        cv=cv, n_jobs=n_jobs, scoring=scoring)\n",
    "\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    plt.semilogx(param_range, train_scores_mean, 'o-', color=\"r\",\n",
    "                 label=\"Training score\")\n",
    "    plt.semilogx(param_range, test_scores_mean, 'o-', color=\"g\",\n",
    "                 label=\"Cross-validation score\")\n",
    "    plt.legend(loc=\"best\")\n",
    "    print(\"Best test score: {:.4f}\".format(test_scores_mean[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test score: 0.7512\n"
     ]
    },
    {
     "data": {
      "image/png": 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mhw4dYvTo0Rw+fBhV9T0OvKAGDRrQpUsX1q5d68t3+PDhzJo1iy5dutCpUyfO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd408992f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=8)\n",
    "param_name = 'max_depth'\n",
    "param_range = [1, 2, 4, 8, 16, 32]\n",
    "\n",
    "plot_validation_curve(clf, X_train, y_train,\n",
    "                      param_name, param_range, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosted Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC Random Forest: 0.9139 +/-0.0027\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=30, max_features=10,\n",
    "                             max_depth=10)\n",
    "\n",
    "scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='roc_auc',\n",
    "                         n_jobs=-1)\n",
    "print(\"ROC Random Forest: {:.4f} +/-{:.4f}\".format(\n",
    "    np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Gradient Boosted Trees: 0.9183 +/-0.0030\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "clf = GradientBoostingClassifier(max_leaf_nodes=5, learning_rate=0.1,\n",
    "                                 n_estimators=100)\n",
    "\n",
    "scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='roc_auc',\n",
    "                         n_jobs=-1)\n",
    "print(\"ROC AUC Gradient Boosted Trees: {:.4f} +/-{:.4f}\".format(\n",
    "    np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation of the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.09 s, sys: 0 ns, total: 2.09 s\n",
      "Wall time: 2.11 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "_ = clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC: 0.9164\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "y_pred_proba = clf.predict_proba(X_test)[:, 1]\n",
    "print(\"ROC AUC: %0.4f\" % roc_auc_score(y_test, y_pred_proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "      <=50K       0.88      0.95      0.91      4918\n",
      "       >50K       0.78      0.59      0.67      1595\n",
      "\n",
      "avg / total       0.85      0.86      0.85      6513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P+eu4+RGn2n86pyAiUoXuuaArrRvV4a9vL+DesZ9RUupkZRgXFbWiWYPcqONV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\no6IgIiIxKgoiIhLz/1zLr0fKALFMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd40877e6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precision_gb, recall_gb, _ = precision_recall_curve(y_test, y_pred_proba)\n",
    "\n",
    "plt.plot(precision_gb, recall_gb)\n",
    "plt.xlabel('precision')\n",
    "plt.ylabel('recall')\n",
    "plt.title('Precision / Recall curve');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variable importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2Bp0KfB+4KyJeXimuReSN4gkRsWAZx61Nxl4l6ZC0BtmyOXL9vigi7qhx7q5z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\nlGQsIiIiokNJxiIiIiI69H/IobdZH0INUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4084191d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "ordering = np.argsort(clf.feature_importances_)[::-1]\n",
    "\n",
    "importances = clf.feature_importances_[ordering]\n",
    "feature_names = features.columns[ordering]\n",
    "\n",
    "x = np.arange(len(feature_names))\n",
    "plt.bar(x, importances)\n",
    "plt.xticks(x + 0.5, feature_names, rotation=90, fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the boosted trees to extract features for a Logistic Regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is an implementation of a trick found in:\n",
    "\n",
    "Practical Lessons from Predicting Clicks on Ads at Facebook\n",
    "Junfeng Pan, He Xinran, Ou Jin, Tianbing XU, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, Joaquin Quiñonero Candela\n",
    "International Workshop on Data Mining for Online Advertising (ADKDD)\n",
    "\n",
    "https://www.facebook.com/publications/329190253909587/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin, clone\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from scipy.sparse import hstack\n",
    "\n",
    "\n",
    "class TreeTransform(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"One-hot encode samples with an ensemble of trees\n",
    "    \n",
    "    This transformer first fits an ensemble of trees (e.g. gradient\n",
    "    boosted trees or a random forest) on the training set.\n",
    "\n",
    "    Then each leaf of each tree in the ensembles is assigned a fixed\n",
    "    arbitrary feature index in a new feature space. If you have 100\n",
    "    trees in the ensemble and 2**3 leafs per tree, the new feature\n",
    "    space has 100 * 2**3 == 800 dimensions.\n",
    "    \n",
    "    Each sample of the training set go through the decisions of each tree\n",
    "    of the ensemble and ends up in one leaf per tree. The sample if encoded\n",
    "    by setting features with those leafs to 1 and letting the other feature\n",
    "    values to 0.\n",
    "    \n",
    "    The resulting transformer learn a supervised, sparse, high-dimensional\n",
    "    categorical embedding of the data.\n",
    "    \n",
    "    This transformer is typically meant to be pipelined with a linear model\n",
    "    such as logistic regression, linear support vector machines or\n",
    "    elastic net regression.\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, estimator):\n",
    "        self.estimator = estimator\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        self.fit_transform(X, y)\n",
    "        return self\n",
    "        \n",
    "    def fit_transform(self, X, y):\n",
    "        self.estimator_ = clone(self.estimator)\n",
    "        self.estimator_.fit(X, y)\n",
    "        self.binarizers_ = []\n",
    "        sparse_applications = []\n",
    "        estimators = np.asarray(self.estimator_.estimators_).ravel()\n",
    "        for t in estimators:\n",
    "            lb = LabelBinarizer(sparse_output=True)\n",
    "            sparse_applications.append(lb.fit_transform(t.tree_.apply(X)))\n",
    "            self.binarizers_.append(lb)\n",
    "        return hstack(sparse_applications)\n",
    "        \n",
    "    def transform(self, X, y=None):\n",
    "        sparse_applications = []\n",
    "        estimators = np.asarray(self.estimator_.estimators_).ravel()\n",
    "        for t, lb in zip(estimators, self.binarizers_):\n",
    "            sparse_applications.append(lb.transform(t.tree_.apply(X)))\n",
    "        return hstack(sparse_applications)\n",
    "\n",
    "\n",
    "boosted_trees = GradientBoostingClassifier(\n",
    "    max_leaf_nodes=5, learning_rate=0.1,\n",
    "    n_estimators=100, random_state=0,\n",
    ") \n",
    "\n",
    "pipeline = make_pipeline(\n",
    "    TreeTransform(boosted_trees),\n",
    "    LogisticRegression(C=1)\n",
    ")\n",
    "\n",
    "pipeline.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC: 0.9233\n"
     ]
    }
   ],
   "source": [
    "y_pred_proba = pipeline.predict_proba(X_test)[:, 1]\n",
    "print(\"ROC AUC: %0.4f\" % roc_auc_score(y_test, y_pred_proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "      <=50K       0.89      0.93      0.91      4918\n",
      "       >50K       0.76      0.66      0.70      1595\n",
      "\n",
      "avg / total       0.86      0.86      0.86      6513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = pipeline.predict(X_test)\n",
    "print(classification_report(y_test, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NrQebvrZuaVU1gjuTQjSwx2U9yS5z1R6oLyKLRWS1iNxQ0oFE5DYRWSUiq1JS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\npZRy0qSglFLKSZOCUkopJ00KSimlnP4f+QNWZT1GXeQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd40bdaf4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precision_gb_lr, recall_gb_lr, _ = precision_recall_curve(\n",
    "    y_test, y_pred_proba)\n",
    "\n",
    "plt.plot(precision_gb, recall_gb, label='GBT')\n",
    "plt.plot(precision_gb_lr, recall_gb_lr, label='GBT + LR')\n",
    "plt.xlabel('precision')\n",
    "plt.ylabel('recall')\n",
    "plt.title('Precision / Recall curve')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:euroscipy-2017-sklearn]",
   "language": "python",
   "name": "conda-env-euroscipy-2017-sklearn-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
